Combining morphological information in a manifold learning framework: Application to neonatal MRI

P. Aljabar; R. Wolz; L. Srinivasan; S. Counsell; J. P. Boardman; M. Murgasova; V. Doria; M. A. Rutherford; A. D. Edwards; J. V. Hajnal; D. Rueckert

doi:10.1007/978-3-642-15711-0_1

Combining morphological information in a manifold learning framework: Application to neonatal MRI

P. Aljabar, R. Wolz, L. Srinivasan, S. Counsell, J. P. Boardman, M. Murgasova, V. Doria, M. A. Rutherford, A. D. Edwards, J. V. Hajnal, D. Rueckert

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

MR image data can provide many features or measures although any single measure is unlikely to comprehensively characterize the underlying morphology. We present a framework in which multiple measures are used in manifold learning steps to generate coordinate embeddings which are then combined to give an improved single representation of the population. An application to neonatal brain MRI data shows that the use of shape and appearance measures in particular leads to biologically plausible and consistent representations correlating well with clinical data. Orthogonality among the correlations suggests the embedding components relate to comparatively independent morphological features. The rapid changes that occur in brain shape and in MR image appearance during neonatal brain development justify the use of shape measures (obtained from a deformation metric) and appearance measures (obtained from image similarity). The benefit of combining separate embeddings is demonstrated by improved correlations with clinical data and we illustrate the potential of the proposed framework in characterizing trajectories of brain development.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
Pages	1-8
Number of pages	8
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-15711-0_1
State	Published - 2010
Externally published	Yes
Event	13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 - Beijing, China Duration: 20 Sep 2010 → 24 Sep 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	6363 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
Country/Territory	China
City	Beijing
Period	20/09/10 → 24/09/10

Access to Document

10.1007/978-3-642-15711-0_1

Cite this

Aljabar, P., Wolz, R., Srinivasan, L., Counsell, S., Boardman, J. P., Murgasova, M., Doria, V., Rutherford, M. A., Edwards, A. D., Hajnal, J. V., & Rueckert, D. (2010). Combining morphological information in a manifold learning framework: Application to neonatal MRI. In Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings (PART 3 ed., pp. 1-8). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6363 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-15711-0_1

Aljabar, P. ; Wolz, R. ; Srinivasan, L. et al. / Combining morphological information in a manifold learning framework : Application to neonatal MRI. Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 3. ed. 2010. pp. 1-8 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

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title = "Combining morphological information in a manifold learning framework: Application to neonatal MRI",

abstract = "MR image data can provide many features or measures although any single measure is unlikely to comprehensively characterize the underlying morphology. We present a framework in which multiple measures are used in manifold learning steps to generate coordinate embeddings which are then combined to give an improved single representation of the population. An application to neonatal brain MRI data shows that the use of shape and appearance measures in particular leads to biologically plausible and consistent representations correlating well with clinical data. Orthogonality among the correlations suggests the embedding components relate to comparatively independent morphological features. The rapid changes that occur in brain shape and in MR image appearance during neonatal brain development justify the use of shape measures (obtained from a deformation metric) and appearance measures (obtained from image similarity). The benefit of combining separate embeddings is demonstrated by improved correlations with clinical data and we illustrate the potential of the proposed framework in characterizing trajectories of brain development.",

author = "P. Aljabar and R. Wolz and L. Srinivasan and S. Counsell and Boardman, {J. P.} and M. Murgasova and V. Doria and Rutherford, {M. A.} and Edwards, {A. D.} and Hajnal, {J. V.} and D. Rueckert",

year = "2010",

doi = "10.1007/978-3-642-15711-0_1",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings",

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Aljabar, P, Wolz, R, Srinivasan, L, Counsell, S, Boardman, JP, Murgasova, M, Doria, V, Rutherford, MA, Edwards, AD, Hajnal, JV & Rueckert, D 2010, Combining morphological information in a manifold learning framework: Application to neonatal MRI. in Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 6363 LNCS, pp. 1-8, 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010, Beijing, China, 20/09/10. https://doi.org/10.1007/978-3-642-15711-0_1

Combining morphological information in a manifold learning framework: Application to neonatal MRI. / Aljabar, P.; Wolz, R.; Srinivasan, L. et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 3. ed. 2010. p. 1-8 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6363 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Aljabar, P.

AU - Wolz, R.

AU - Srinivasan, L.

AU - Counsell, S.

AU - Boardman, J. P.

AU - Murgasova, M.

AU - Doria, V.

AU - Rutherford, M. A.

AU - Edwards, A. D.

AU - Hajnal, J. V.

AU - Rueckert, D.

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AB - MR image data can provide many features or measures although any single measure is unlikely to comprehensively characterize the underlying morphology. We present a framework in which multiple measures are used in manifold learning steps to generate coordinate embeddings which are then combined to give an improved single representation of the population. An application to neonatal brain MRI data shows that the use of shape and appearance measures in particular leads to biologically plausible and consistent representations correlating well with clinical data. Orthogonality among the correlations suggests the embedding components relate to comparatively independent morphological features. The rapid changes that occur in brain shape and in MR image appearance during neonatal brain development justify the use of shape measures (obtained from a deformation metric) and appearance measures (obtained from image similarity). The benefit of combining separate embeddings is demonstrated by improved correlations with clinical data and we illustrate the potential of the proposed framework in characterizing trajectories of brain development.

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SN - 9783642157103

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Aljabar P, Wolz R, Srinivasan L, Counsell S, Boardman JP, Murgasova M et al. Combining morphological information in a manifold learning framework: Application to neonatal MRI. In Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 3 ed. 2010. p. 1-8. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-642-15711-0_1

Combining morphological information in a manifold learning framework: Application to neonatal MRI

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